increasing capacity to share registry data€¦ · national eresearch collaboration tools and...

Increasing capacity to share registry data

David GroenewegenDirector, Research InfrastructureMonash University Library

Outline

� Some background on data management in Australia

� Data management at Monash

� Taking data management forward into Research Infrastructure

� Medical registries and some questions

24 May 2013Increasing capacity to share registry data - David G roenewegen 2

Australian Code for the Responsible Conduct of Research

� Among other things, describes the responsibilities of institutions and researchers in the management of research data and primary materials

� Institutions are to retain research data, provide secure data storage, identify ownership, and ensure security and confidentiality of research data

� Researchers are to retain research data and primary materials, manage storage of research data and primary materials, maintain confidentiality of research data and primary materials

� All 39 Australian universities are signatories


Data management

• Code outlines data responsibilities of researchers and their institutions

• Good research data management practices ensure that researchers and institutions are:

• able to meet their obligations to funders, • improve the efficiency of research, and • make data available for sharing, validation and

re-use.


Selected data investments• Recognition at government level of the importance of data and data

re-use

• Substantial funding since 2007, current projects include:

• $75.5M investment into data re-use infrastructure and capability building - Australian National Data Service (ANDS)

• $50M investment into national research data storage –Research Data Storage Infrastructure (RDSI)

• $47M investment into collaborative research infrastructure -National eResearch Collaboration Tools and Resources (NeCTAR)


To west coast

United States

To

Singapore

The Australian national e-infrastructure platform

Primary Node15

participants

Additional Node

4 participants

Primary Node

11 participants

Primary Node

4 participants

Primary Node

8 participants

Primary Node

6 participants

Additional Node

7 participants

Primary Node

6 participants

Cloud 50,000 concurrent tasks

Data 10-20 PB fast growing

Data 1-2 PB more focused

HPC > 1 Pf/s capability

HPC ~100 Tf/s specialised

Network Nx100 Gb/s layer-1

Network 10 Gb/s layer-3

Slide by Dr. Rhys Francis

(Executive Director, Australian eResearch Infrastructure Council (AeRIC)

)


ANDS as a funder� Funding primarily provided at institutional level

� Infrastructure programs

– Metadata Stores– Data Capture– Public Sector Data– National Collections– Applications

� Human capacity programs

– Seeding the Commons– Frameworks & Capabilities


Tools, frameworks and capability � improved institutional guidance for internal institutional data management

� improved institutional guidance for responding to national instruments such as the Australian Code for the Responsible Conduct of Research

� institution wide research data management planning frameworks or improved research data management at many research institutions

� increased institutional capability for research data management with staff trained in this area

� tools that enable more effective re-use of research data

� tools to automatically capture rich metadata along with the data for a wide range of instruments

� operational metadata stores for this metadata

� collections descriptions feeds to ANDS, both Research Institutions and Public Sector data holders


RedMap� Observations of marine

organisms by non-scientists can be added to traditional scientific sources

� Researcher had just picked up an unusual observation of a gloomy octopus (Octopus tetricus), traditionally found off Sydney and the east coast of Australia but not in Tasmania.

� Mexican graduate student now looking at why.

924 May 2013Increasing capacity to share registry data - David G roenewegen


Priority 4.3: Development of new models for research training that explicitly focus on the professional employment needs of graduates.

Changes in the conduct of research activity to reflect more globally oriented, collaborative and multi-disciplinary approaches necessitate reassessment of the quality of our research training programs.

Why data management is important� you can make your life easier and less stressful

� you might avoid catastrophe .

� you need to ride the ‘data wave’ not drown in a sea of irrelevance

� you may want to use your research data again

� you may need to justify your claims

� you can enhance your opportunities to ‘make friends and influence people’

� you can receive credit for the data you produce

� you may be required to do so


Monash University

� Large - ~3,800 academic staff, ~4,200 higher degree by research students

� Diverse – 10 faculties, many disciplines, 6 Victorian and 2 overseas campuses

� Research intensive - $282 million in externally funded research income in 2011

� Strong history of leadership in Australasia in e-research & scholarly communications, including research data management, with active Library involvement


Statement of intentMonash University recognises that research data that is better

managed , more discoverable and available for re-use will contribute to increased research impact , enhanced research practice (including collaboration) and improved education outcomes.

The University aims to maintain its national leadership role around research data management and to fulfil compliance requirements and community expectations .

All members of the Monash University community share responsibility to improve research data management in a coordinated and integrated way.

This strategy supports the research, education and professional services strategies developed as part of the Monash Futures program.


Research data management by 201524 May 2013Increasing capacity to share registry data - David G roenewegen 14

Excellence and impact

•More research data discoverable and available for re-use

•Re-use of Monash data contributes to formal and informal measures of research quality and impact

•Systems and policies help make research data available more quickly and easily

World class infrastructure

• Systems and facilities that support data management are expanding and improving

•Researchers make more use of these systems and facilities

•A range of institutional and discipline-specific needs are catered for

• Local infrastructure leverages national and international services and facilities

• Infrastructure supports the management of data, regardless of format

Skills and knowledge

•Researchers have the knowledge and skills they need to manage data well, and understand the benefits of making data discoverable and available for re-use

•Data management skills are seen as essential for research and transferable to other workplaces

•Research data contributes to the educational outcomes of students from an early stage in their academic career

•Data management professionals have career paths and development opportunities

•Professional development opportunities meet the needs of researchers from different disciplines and at different career stages

Integrity and professionalism

•Managing data well is seen as a key part of research integrity and professional practice

•Compliance with Section 2 of the Code for Responsible Conduct of Research is improved

•All Monash researchers understand their obligations and take practical steps - as individuals and teams - to improve how research data is managed

•Research data management advisory and technical services are increasingly coordinated and integrated

Leadership and collaboration

•Monash University leads and actively participates in global, national and regional research data initiatives

•The University is regarded as a partner of choice for collaborative work in this area

•Data management technologies developed at Monash University are succesfully adopted by other organisations

•Monash University is seen by other institutions as an authoritative source of information and advice


Guidelines provide advice on common concerns

Implementation

� Combination of top-down and bottom-up

� Top-down – communications plan, senior managers workshop, infrastructure

� Bottom-up – hands-on

– Faculties, e.g. Pharmacy, Arts– School or centre, e.g. Biochemistry and Molecular

Biology, Centre for Organisational and Social Informatics

– Workgroup, – Early career researchers


� Focus on achievable steps and simple solutions

– Faculty Research Committee RDM working group• ADR (chair), researchers, faculty information

technology, research manager, • Library and eResearch staff are there to remove

barriers, encourage and share information– Two sub-groups for digital data storage and protocols

for file structure and naming– Mapping procedures to local practices to identify good

practices, areas for improvement and priorities– Induction/training of early career researchers


Case study: Faculty of Pharmacy & Pharmaceutical Sciences

Forcing ourselves to get in early with addressing the challenges of RDM has been very valuable… ‘Wins’ – such as central storage that is easily accessible by all staff – are proving to be much more powerful than perhaps we first thought. Notably, these systems are starting to focus attention on behaviours, and in many respects are changing the way data is viewed, shared, accessed etc. So a physical solution is actually changing the way we work – which in the long run will be a more useful benefit than safe storage. We have a long way to go – but I think the Faculty is starting to see this as a benefit rather than a regulatory checkbox and ultimately this is where we will have the most impact.

- Chris Porter, Associate Dean of Research, Pharmacy


Outreach

� Contact librarians and learning skills advisers are partnering with academics

� Work underpinned by theory in the form of the Research Skills Development Framework

� Information research skills regularly addressed in unit reviews, skill diagnostics, curriculum design, assessment task design, marking rubrics, etc.

� Emphasis on research skills from undergrad through Honours and on to higher degrees


Research Infrastructure• Exploring synergies between:

• Research data management

• Research repositories

• Digitisation

• Publishing

to ensure ease of use.

• Developing relationships within the university’s research community

• Researchers

• Library, Monash eResearch Centre, Research Office, eSolutions etc.


What this will look like

• Research data management is undertaken invisibly through:

• Incorporation into training and engagement• Provision of services for all researchers

• Storage• Advice• Publication• Tools


Publishing and discovery� Making data available online

– Local storage– RDSI - Nodes– Access control– Citable (DOI)– Licencing - AusGOAL

� Discovery

– Data registries – Research Data Australia– Google– Links to publications


Monash University Research Repository� ~91,000 items

– Publications required for Excellence in Research Australia (ERA)

– Journal collections– Research data– Newspapers – Lot’s Wife– Monash University patents– PhD theses– Faculty of Business and Economics working papers– 16% (~15,000) are Open Access


24 May 2013 Increasing capacity to share registry data - David

Groenewegen

24

What I don’t know about medical registries

� How often are they updated? Or are they static?

� How do people find them?

� How are they supported by your institutions?

� Do people cite them? In what form? Do they just cite parts?

� What sort of permissions are there around usage?

� How do you deal with ethics?

� How do you anonymise data?


THANKS – [email protected]


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